ASIS&T;
Since 1937, ASIS&T; has been the society for information professionals leading the search for new and better theories, techniques, and technologies to improve access to information.
The Secret Language of Data — Taxonomies and Meaningful Metadata to Crack the Cipher and Tell the Story
In December 2008 the European Parliament and the Council adopted a framework regulation for statistics concerning public health and health and safety at work. The magnitude of data collected this far is enormous, however none of the available databases—WHO, Health-EU or Eurostat, provides a usable interface that allows users to generate customized and easy to interpret health statistics. Additionally, it is virtually impossible to demonstrate the relationship between health topics and social or environmental factors to help contextualize data and substantiate correlation or prove causation.
This case study, drawn from direct professional experience, illustrates how correlation is discernable and actionable. And how employing taxonomies and metadata to improve navigation, browsing, search and discovery results in understanding and augmented awareness.
The California Health Interview Survey and its online statistical analysis query tool, AskCHIS, have managed to bridge the gap between the collection of large amounts of health and socioeconomic indicators and its meaningful visualization. The query tool, with its simple and intuitive interface, has effectively become a benchmark for health data statistical analysis tools that are usable across a variety of backgrounds and skills.
In an attempt to better support its mission, the first version of the tool offered distinct basic and advanced interfaces, but qualitative user research demonstrated that even the most statistics-savvy users preferred the simpler layout, which was further developed into its current build. A rigorous taxonomy of variables, questionnaires and data dictionaries entries, in addition to meaningful metadata give the users the option to browse the full hierarchy of terms and categories or use keyword and phrases search. Supporting documentation—in the form of methodology papers, survey topics, questionnaires and data dictionaries, helps to contextualize research.
Alberta Soranzo
Alberta’s relationship with interfaces begun when she was a child in Italy and was employed as a human guinea pig by Olivetti. Since then, she has not stopped testing, critiquing, and making things better.
She has been based in the US for 15 years, and is manager of user experience and strategy at the Center for Health Policy Research at UCLA. She specializes in the development of visual frameworks that support retrieval of information across large data sets and develops strategies, workflows and interfaces for public health. Her current projects focus on statistical analysis online query tools and survey data retrieval visualization, and she is passionate about simplicity, findability and user journeys. Alberta also consults on strategy and IA for the Robert F. Kennedy Center for Justice & Human Rights.
When she’s not spending time with her family or working, you can find her chasing a hockey puck on the ice or being otherwise mischievous.
Alberta writes at albertasoranzo.com and tweets at @albertatrebla.
